crime_data = {}
drug_crime_snippet = datasets['Drug_Crime'].iloc[:2000]
# Iterate over the DataFrame rows and populate the dictionary
for index, row in drug_crime_snippet.iterrows():
values = {
'Police_Depart': row['NYC Penal Code'],
'Coordinate': [row['Lat_Lon'][0],row['Lat_Lon'][1]]
}
crime_data[index] = values
PD_values = [value['Police_Depart'] for value in crime_data.values() ]
# Displaying the extracted values
max_PD = max(PD_values)
min_PD = min(PD_values)
# Create a color scale for populationa
color = ['yellow','green' ,'orange', 'red','purple','brown']
color_scale_dot = folium.LinearColormap(colors=color, vmin=min_PD, vmax=max_PD)
# Creating Shapely polygons for each district
district_polygons = {}
for feature in NYC_district['features']:
boro_cd = feature['properties']['boro_cd']
coordinates = feature['geometry']['coordinates'][0][0]
polygon = Polygon(coordinates)
if boro_cd not in district_polygons:
district_polygons[boro_cd] = MultiPolygon() # Initialize MultiPolygon for each district
district_polygons[boro_cd] = district_polygons[boro_cd].union(polygon)
crime_data_switch = {}
# Iterate over the DataFrame rows and populate the dictionary
for index, row in drug_crime_snippet.iterrows():
values = {
'Police_Depart': row['NYC Penal Code'],
'Coordinate': [row['Lat_Lon'][1],row['Lat_Lon'][0]]
}
crime_data_switch[index] = values
# Initialize an empty dictionary to store the count of incidents per boro_cd
incident_num_dict = {}
# Iterate through crime incidents and check which district they belong to
for crime_id, crime_info in crime_data_switch.items():
crime_coords = Point(crime_info['Coordinate'])
for boro_cd, polygon in district_polygons.items():
if polygon.contains(crime_coords):
# Increment the count of incidents for this boro_cd
if boro_cd in incident_num_dict:
incident_num_dict[boro_cd] += 1
else:
incident_num_dict[boro_cd] = 1
break
# Add districts with no incidents and set their count to 0
for boro_cd in district_polygons.keys():
if boro_cd not in incident_num_dict:
incident_num_dict[boro_cd] = 0
incident_num = [value for value in incident_num_dict.values() ]
# Displaying the extracted values
max_num = max(incident_num)
min_num = min(incident_num)
# Create a color scale for population
color = ['yellow','green' ,'orange', 'red','purple','brown']
color_scale_incident = folium.LinearColormap(colors=color, vmin=min_num, vmax=max_num)
# Create a map centered at New York City
Map_NYC = folium.Map(location=[40.7128, -74.0060], zoom_start=10, tiles='CartoDB positron')
# Add GeoJSON layer to the map
folium.GeoJson(
NYC_district,
name='nyc-districts & crime number',
style_function= lambda feature: {
'fillColor': color_scale_incident(incident_num_dict.get(feature['properties']['boro_cd'])) ,
'color': 'black',
'weight': 1,
'fillOpacity': 0.7
},
).add_to(Map_NYC)
# Add a marker with text at a specific location
folium.map.Marker(location=[40.76440432106826, -74.02372202390437], icon=folium.DivIcon(html=f"<div style='font-size: 15pt'>Manhattan</div>")).add_to(Map_NYC)
folium.map.Marker(location=[40.73536409760489, -73.85770225871684], icon=folium.DivIcon(html=f"<div style='font-size: 15pt'>Queens</div>")).add_to(Map_NYC)
folium.map.Marker(location=[40.64209332024007, -74.00113449211089], icon=folium.DivIcon(html=f"<div style='font-size: 15pt'>Brooklyn</div>")).add_to(Map_NYC)
folium.map.Marker(location=[40.60535622898584, -74.16354960167357], icon=folium.DivIcon(html=f"<div style='font-size: 15pt'>Staten Island</div>")).add_to(Map_NYC)
folium.map.Marker(location=[40.857210423454255, -73.90217959092037], icon=folium.DivIcon(html=f"<div style='font-size: 15pt'>Bronx</div>")).add_to(Map_NYC)
# Add legend for population color scale
color_scale_incident.caption = 'Crime Category'
color_scale_incident.add_to(Map_NYC)
Map_NYC
